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Record W2003405159 · doi:10.1002/jcb.20105

Incorporating pathologists' criteria of malignancy into the evolutionary model for cancer development

2004· review· en· W2003405159 on OpenAlexaff
Andrew H. Fischer, Kyle A. Young, Ronald A. DeLellis

Bibliographic record

VenueJournal of Cellular Biochemistry · 2004
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMalignancyCancerBiologyCancer cellPhenotypeComputational biologyGeneGenetics

Abstract

fetched live from OpenAlex

A wide variety of alterations in cell and tissue structure still form the basis for cancer diagnosis by pathologists. Cancer development is recognized to be an evolutionary process [Foulds, 1954; Cairns, 1975; Nowell, 1976; Sager, 1982; Tomlinson et al., 1996; Cahill et al., 1999; Tomlinson and Bodmer, 1999], but the phenotypic changes diagnostic of cancer (pathologists' "criteria of malignancy") have not been integrated into the existing evolutionary framework. Since phenotypic changes bear an important relationship to the genetic and physiologic changes underlying Darwinian evolution, we propose that diagnostic structural alterations also bear an important and predictable relation to both the cancer genes and the functional alterations active at any particular step in the development of a cancer. Cancer genes are predicted to mediate the acquisition of cellular-level diagnostic criteria and the diagnostic cellular-level structural changes should reflect in a useful manner the altered cell physiology required for the cell to achieve increased "cellular fitness" at any particular step of colonal evolution. Tissue-level criteria of malignancy should relate less directly to specific cancer genes, but tissue-level criteria should still provide essential insight into the interplay of the altered cellular fitness with the constraints imposed by the cells' microenvironment. The evolutionary framework allows tissue-level criteria of malignancy to be expressed in terms of viable hypotheses for the mechanism of clonal expansion at any particular step in cancer development. This approach to conveying the tissue-level criteria of malignancy complements pattern recognition approaches to diagnosis, and establishes common ground between pathology and cell biology. When viewed from this perspective, the functions of cancer genes appear quite different from those predicted by the "Gatekeeper, Caretaker" or "Hallmarks of Cancer" models. Finally, a full evolutionary framework incorporating the criteria of malignancy restores congruity between the histogenetic classification and the emerging molecular classification of cancer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.317
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2004
Admission routes1
Has abstractyes

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